Using Urban Canopy Designs to Improve Air Quality and Public Health in Metropolitan Areas of the United States: The Healthy Trees, Healthy People program at Portland State University aims to find canopy designs that most effectively improve the public's health. With generous support from the United States Forest Service (USFS) and several participating organizations, an interdisciplinary research team will collaborate through 2014 to quantify the health benefits of the urban forest and their role in addressing air pollution and urban heat across 13 cities of the United States.

The three main research questions to be answered are:

How do different canopy designs (type, composition, distribution, and location of vegetation) improve air quality and reduce the heat island formation in urban landscapes?

Which canopy designs are best for reducing the incidence of acute respiratory illness in neighborhood residence most exposed to air pollutants?

Which canopy designs are most promising for reducing health care costs to the United States?

A growing body of research is drawing the link between human health outcomes and the presence - or absence - of trees. Urban street trees slow traffic, provide sidewalk shade, improve air quality, and reduce the urban heat island effect, contributing to improved health outcomes for children, older adults, and those living in poverty. Air quality vulnerability varies between neighborhoods - and so does the presence of trees - but new trees are rarely planted with these variations in mind.

The Trees and Health APP was designed to make it easy to plant trees in a targeted way, and to take neighborhood vulnerability into account when prioritizing planting locations. Using freely available data from the US Census, TIGER/Line, National Elevation Dataset and the National Land Cover Dataset, the Trees and Health APP makes it easy to see variations in canopy cover, air quality, urban heat, and vulnerable populations anywhere in 13 cities. Addition of more cities to the tool will begin a ta later date.

How valid are the air quality predictions?

This project grew out of air quality research conducted in the Portland, OR region in 2013 (Rao 2014). The level of NO2, a strong marker for human-generated air pollution, was measured at 144 locations, enabling a spatially-fine prediction of traffic-related air pollution from land use and transportation network data. We applied the model to 13 cities across the United States, and compared our model results (TRAQ, or traffic-related air quality) to NO2 measurements from the US EPA. The table below shows the results:

City

EPA

Predicted

Error

Albuquerque

7.8

7

-0.8

Baltimore

19

18.2

-0.8

Cincinnati

11.2

12.2

1

Denver 1

24.6

24.7

0.1

Denver 2

22.1

10.4

-11.7

Houston 1

8.1

12.5

4.4

Houston 2

5

10.4

5.4

Houston 3

9.7

14.6

4.9

Houston 4

8.1

11

2.9

Houston 5

7.4

9

1.6

Houston 6

8.1

13.3

5.2

Houston 7

10.9

11.3

0.4

Minneapolis

13.8

27.7

13.9

Orlando

3.7

12

8.3

Phoenix 1

11.9

10.9

-1

Phoenix 2

18.2

15.7

-2.5

Phoenix 3

10.8

11.1

0.3

Phoenix 4

13.2

14.7

1.5

Pittsburgh 1

11.2

18

6.8

Pittsburgh 2

8.2

13.4

5.2

Portland

7.1

12.2

5.1

Sacramento 1

6.2

13.4

7.2

Sacramento 2

5.6

10.8

5.2

Tampa

4.1

9.7

5.6

Average:

10.7

13.5

2.8

StandardDev:

5.505859856

4.711956731

4.833300849

Several factors contribute to the differences between TRAQ and measured NO2 levels. The EPA values are a monthly mean from August, while the original study captured for 12 days at the end of August and beginning of September. Air quality varies over the course of each year, and variations in geography and tree species composition between cities mean this cycle is not the same everywhere. TRAQ predicts mobile sources of air pollutants, but it does not incorporate point sources of air pollution, like a coal-fired power plant or refinery. Canopy data comes from the NLCD, and includes predicted canopy cover as a percent of area at a 30 meter resolution. This doesn't allow for tree species-specific analysis.